基于扰动观测器的桥式起重机匹配与不匹配扰动离散滑模预测控制

IF 1.7 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Rong Shi, Huimin Ouyang
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引用次数: 0

摘要

在实际工作环境中,欠驱动桥式起重机容易受到机械摩擦、不确定参数等扰动的不利影响。特别是,如果有效载荷受到干扰(即不匹配的干扰),将发生不可预测的摆动。为此,提出了一种基于扰动观测器的桥式起重机离散时间滑模预测控制方法。具体来说,为了消除不确定的干扰,我们提出了一个干扰观测器来估计和补偿输入信道的干扰。然后,通过在离散滑模曲面上加入干扰误差估计量,得到了新的离散滑模控制律。为了减少滑模固有的抖振,我们将离散滑模律集成到模型预测控制中,并对预测控制进行滚动优化和反馈修正,最后输出最优控制律作用于系统。该控制方法不仅能进一步提高控制精度,而且具有足够的鲁棒性。仿真和各种条件下的鲁棒性实验验证了所提控制器的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disturbance-observer-based discrete-time sliding mode predictive control for overhead cranes with matched and unmatched disturbances
In actual working environments, underactuated overhead cranes are susceptible to the adverse effects of the mechanical friction, uncertain parameters, and other disturbances. In particular, if the payload is disturbed (i.e. the unmatched disturbance), unpredictable swings would occur. Therefore, a discrete time sliding mode predictive control for overhead cranes based on a disturbance observer is proposed. Specifically, to eliminate uncertain disturbances, we propose a disturbance observer to estimate and compensate the disturbance to the input channel. Then, a new discrete sliding mode control law is obtained by adding the disturbance error estimator to the discrete sliding mode surface. In order to reduce the chattering inherent in the sliding mode, we integrate the discrete sliding mode law into the model predictive control, and use the rolling optimization and feedback correction of the predictive control, and finally output the optimal control law to act on the system. This control method can not only further improve the control accuracy, but also has sufficient robustness. Simulation and robustness experiments under various conditions verify the effectiveness of the proposed controller.
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来源期刊
CiteScore
4.10
自引率
16.70%
发文量
203
审稿时长
3.4 months
期刊介绍: Transactions of the Institute of Measurement and Control is a fully peer-reviewed international journal. The journal covers all areas of applications in instrumentation and control. Its scope encompasses cutting-edge research and development, education and industrial applications.
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